Wheel Odometry

Wheel odometry, the estimation of a vehicle's position and orientation using wheel encoder data, is crucial for robotic navigation, particularly in environments where GPS or other exteroceptive sensors are unreliable. Current research focuses on improving accuracy and robustness by fusing wheel odometry with other sensor modalities (e.g., IMUs, LiDAR, cameras) using advanced filtering techniques like Kalman filters and neural networks, often within a factor graph optimization framework. These improvements are vital for enhancing the reliability of autonomous systems, such as self-driving cars, agricultural robots, and assistive devices like wheelchairs, in diverse and challenging operational conditions.